{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":14,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":14,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"d2dce2612f16","filters":{"venue":"Statisctics and computing/Statistics and computing"}},"results":[{"id":"W982522955","doi":"10.1007/978-1-4614-9020-3_8","title":"Programming in R","year":2013,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Data Analysis with R","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Programming language; Computer science; Programming paradigm; Order (exchange); Programming domain; C programming language; Object-oriented programming; Inductive programming; Artificial intelligence; Software","authors":[{"name":"Pierre Lafaye de Micheaux","is_ca":true},{"name":"Rémy Drouilhet","is_ca":false},{"name":"Benoît Liquet","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01385810052166029,"gpt":0.2451940253181226,"spread":0.2313359247964624,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003231557,0.001923056,0.001930588,0.001947266,0.0006704515,0.004374498,0.00196688,0.001176709,0.1091987],"category_scores_gemma":[0.01367703,0.001311984,0.001635925,0.002145698,0.001298473,0.002610096,0.001739858,0.003534074,0.1488043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005753277,"about_ca_system_score_gemma":0.001591137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009087429,"about_ca_topic_score_gemma":0.001245796,"domain_scores_codex":[0.9960073,0.001781191,0.0004106285,0.0006965392,0.0009580019,0.000146368],"domain_scores_gemma":[0.99324,0.004059293,0.0002922079,0.0014753,0.0008001822,0.0001330172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004442428,0.00003159516,0.0001928545,0.0009443962,0.00008038124,0.0001037943,0.0001573805,0.004669054,0.0008948308,0.2769138,0.4414925,0.2744751],"study_design_scores_gemma":[0.0000389592,0.00002275124,0.0001472717,0.0003250589,0.00004809791,0.0003042394,0.00004883127,0.01324471,0.001865884,0.3073719,0.6765426,0.00003975641],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003419749,0.001578828,0.9013045,0.001251184,0.0005551502,0.000128443,0.004244797,0.02973781,0.06085732],"genre_scores_gemma":[0.01068409,0.002180889,0.8733022,0.001568856,0.000628639,0.00104635,0.006640485,0.02941247,0.07453606],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1091987,"threshold_uncertainty_score":0.3653059,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385489859","doi":"10.1007/978-3-031-33390-3_10","title":"Random Forests","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Random forest; Sample (material); Mathematics; Statistics; Tree (set theory); Bootstrap aggregating; Artificial intelligence; Machine learning; Computer science; Combinatorics","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09674985517006617,"gpt":0.3869100027923107,"spread":0.2901601476222446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00147982,0.001276089,0.001057578,0.001882311,0.0008375407,0.002253399,0.002047942,0.001134965,0.0734005],"category_scores_gemma":[0.004130608,0.0008110506,0.001533668,0.001978231,0.0004031154,0.001973023,0.001308312,0.001687763,0.07146852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003439257,"about_ca_system_score_gemma":0.0008967081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001314498,"about_ca_topic_score_gemma":0.00437299,"domain_scores_codex":[0.9985377,0.0003000735,0.00006055916,0.0004018875,0.0005807498,0.0001190148],"domain_scores_gemma":[0.9985379,0.0005854672,0.00006536864,0.0003930832,0.000347387,0.0000708853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001121989,0.0001307236,0.0006522289,0.0004114774,0.000144545,0.0001003189,0.00005695434,0.02147305,0.003145131,0.05717362,0.2968336,0.6197662],"study_design_scores_gemma":[0.00006204169,0.0000743261,0.0007889297,0.0002312185,0.0001158112,0.0005797431,0.00005856398,0.1755658,0.009497753,0.1792925,0.6336591,0.00007435559],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002224691,0.002016836,0.8850738,0.0004265046,0.0007087463,0.00026087,0.006874861,0.01879433,0.08361936],"genre_scores_gemma":[0.03256727,0.00184027,0.8057572,0.0008518379,0.0004756382,0.000513213,0.02264339,0.006311033,0.1290401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0734005,"threshold_uncertainty_score":0.2455491,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486382","doi":"10.1007/978-3-031-33390-3_12","title":"Support Vector Machines","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperplane; Support vector machine; Mathematics; Real line; Separable space; Space (punctuation); Quadratic equation; Line (geometry); Kernel (algebra); Class (philosophy); Artificial intelligence; Combinatorics; Computer science","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01799468312861486,"gpt":0.2646509473033243,"spread":0.2466562641747095,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006997202,0.00119234,0.001223931,0.001220179,0.0004082978,0.002018939,0.001288545,0.001045461,0.01658674],"category_scores_gemma":[0.003771163,0.000413139,0.0006384736,0.00184838,0.0003531947,0.001725216,0.000957204,0.001492302,0.01816757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002881127,"about_ca_system_score_gemma":0.0006315338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007748769,"about_ca_topic_score_gemma":0.0009601319,"domain_scores_codex":[0.9989552,0.0001843021,0.0000627751,0.0002517617,0.0004768644,0.00006912134],"domain_scores_gemma":[0.9987875,0.0004278377,0.00007539903,0.0002572839,0.0004105061,0.0000415279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006240769,0.00007269513,0.0003421266,0.0001399344,0.00004759423,0.00003515542,0.00001977483,0.01939593,0.001809529,0.01321558,0.03379633,0.9310629],"study_design_scores_gemma":[0.00003335017,0.000161542,0.001081164,0.0001290704,0.00006181467,0.0002692761,0.00007007812,0.7879112,0.01069271,0.08048027,0.1190605,0.00004899152],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007638906,0.004774753,0.9377298,0.0007286331,0.0008237329,0.0001639698,0.00147429,0.007398502,0.03926751],"genre_scores_gemma":[0.2002847,0.004993306,0.6662566,0.0007086195,0.001254826,0.0004125859,0.008913992,0.001020366,0.116155],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01658674,"threshold_uncertainty_score":0.05548823,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486542","doi":"10.1007/978-3-031-33390-3_4","title":"Logistic Regression","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Logistic regression; Statistics; Covariate; Mathematics; Logistic model tree; Econometrics; Computer science; Artificial intelligence","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1867757406559741,"gpt":0.4257310844756461,"spread":0.238955343819672,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689873,0.0008909798,0.0006106045,0.001192863,0.0004186821,0.001719379,0.001337037,0.0008571812,0.09487865],"category_scores_gemma":[0.00781088,0.0004119616,0.0006987073,0.002006202,0.0004165243,0.001438878,0.001124029,0.001847824,0.07434159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005336863,"about_ca_system_score_gemma":0.0008880736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605447,"about_ca_topic_score_gemma":0.002659941,"domain_scores_codex":[0.9991869,0.0003061132,0.00002981546,0.0001579559,0.0002778366,0.00004137011],"domain_scores_gemma":[0.9985685,0.0008269755,0.00008234627,0.0002193899,0.0002492447,0.00005344217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006573483,0.00006470275,0.002398019,0.0002639818,0.00007356871,0.0002160133,0.0001530021,0.00198405,0.0004486004,0.1073638,0.3463537,0.5406149],"study_design_scores_gemma":[0.00002254944,0.00006307097,0.003581902,0.0002920768,0.00006657188,0.001467048,0.0001631838,0.0118794,0.001143275,0.1137457,0.8675296,0.00004564536],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.006366573,0.01130435,0.3771699,0.008972376,0.001954868,0.0002185124,0.005790576,0.004499887,0.5837229],"genre_scores_gemma":[0.05383102,0.009883529,0.08078332,0.002177132,0.001153464,0.0003369479,0.006845637,0.001614186,0.8433748],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.09487865,"threshold_uncertainty_score":0.3174007,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486861","doi":"10.1007/978-3-031-33390-3_9","title":"Trees","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Decision tree; Mathematics; Rectangle; Combinatorics; Boosting (machine learning); Artificial intelligence; Computer science; Discrete mathematics; Theoretical computer science","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02492851284539378,"gpt":0.2649124460204725,"spread":0.2399839331750788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000392828,0.0006955042,0.0006690198,0.001569309,0.001083495,0.002548423,0.001273014,0.0009600953,0.1177007],"category_scores_gemma":[0.002439893,0.0004715679,0.0008576813,0.00217028,0.0005359662,0.002824315,0.001430232,0.001747407,0.08614986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005525561,"about_ca_system_score_gemma":0.0008952919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210587,"about_ca_topic_score_gemma":0.002429598,"domain_scores_codex":[0.9993299,0.0000726621,0.00002989604,0.000204493,0.0002834924,0.0000794972],"domain_scores_gemma":[0.9991731,0.0001978249,0.00003595784,0.0002960417,0.000232972,0.00006404264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009506943,0.00007046348,0.0004355748,0.0002782537,0.00003412871,0.00008329147,0.0001425052,0.0042574,0.003088072,0.2454788,0.3077825,0.438254],"study_design_scores_gemma":[0.00002184387,0.00002826807,0.000248028,0.00008623746,0.00002621364,0.0002093901,0.00006202832,0.01285753,0.002830827,0.2468451,0.7367676,0.00001695382],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0078455,0.002962476,0.4987082,0.002105481,0.001645836,0.0004722878,0.02190158,0.01460255,0.4497561],"genre_scores_gemma":[0.08321404,0.003616927,0.3569956,0.00214611,0.000784254,0.0006398713,0.0443256,0.006307551,0.5019701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1177007,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385489627","doi":"10.1007/978-3-031-33390-3_14","title":"Neural Networks","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Backpropagation; Artificial neural network; Normalization (sociology); Regularization (linguistics); Gradient descent; Stochastic gradient descent; Computer science; Artificial intelligence; Feedforward neural network; Nonlinear system; Dropout (neural networks); Feed forward; Algorithm; Mathematics; Pattern recognition (psychology); Machine learning","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02097023155890035,"gpt":0.2522566464519125,"spread":0.2312864148930122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000273611,0.0006537953,0.0005068755,0.0006265496,0.0003145273,0.001474358,0.0009222092,0.001011684,0.02768208],"category_scores_gemma":[0.001330028,0.0002931022,0.0002995481,0.0008008624,0.0004233866,0.001430955,0.0007060664,0.001050023,0.01235843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004815368,"about_ca_system_score_gemma":0.000482152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00181597,"about_ca_topic_score_gemma":0.002570614,"domain_scores_codex":[0.9997796,0.00002974417,0.0000106181,0.00005879845,0.000103833,0.00001737303],"domain_scores_gemma":[0.9997769,0.00006536149,0.00001403816,0.00005029078,0.00008301112,0.00001028432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003991209,0.00003453557,0.0003162027,0.0002693038,0.00005321874,0.00007107152,0.00003700966,0.06183505,0.00321598,0.1551064,0.06662439,0.7123969],"study_design_scores_gemma":[0.00001635558,0.00005375806,0.0006491442,0.0002112584,0.00005865113,0.0002667152,0.00004675024,0.427835,0.007026293,0.2570624,0.3067362,0.00003748764],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.006609493,0.01819362,0.6631457,0.002377307,0.002635745,0.0001114135,0.001183634,0.002558974,0.3031841],"genre_scores_gemma":[0.1852287,0.01906555,0.2273573,0.001300247,0.001378475,0.0003191285,0.002794788,0.0007154063,0.5618404],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02768208,"threshold_uncertainty_score":0.09260583,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385489676","doi":"10.1007/978-3-031-33390-3_3","title":"Statistical Learning: Practical Aspects","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Variance (accounting); Bayes' theorem; Computer science; Scaling; Artificial intelligence; Algorithm; Machine learning; Variable (mathematics); Statistical learning; Mathematics; Statistics; Data mining; Bayesian probability","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03189300187760231,"gpt":0.318637779957082,"spread":0.2867447780794797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00478188,0.00144386,0.001249677,0.001486656,0.000822165,0.003731823,0.001936016,0.002005601,0.0162811],"category_scores_gemma":[0.01646411,0.000952311,0.0006649886,0.001748095,0.005122779,0.005960046,0.002737005,0.005194008,0.0132174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190196,"about_ca_system_score_gemma":0.001304698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001328819,"about_ca_topic_score_gemma":0.001378576,"domain_scores_codex":[0.9971948,0.001298039,0.0001629349,0.0003062871,0.0009821367,0.00005575816],"domain_scores_gemma":[0.9879791,0.009517098,0.0001193274,0.00123038,0.001023449,0.0001306697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001272461,0.00005739757,0.0002281113,0.0003669448,0.00003159016,0.0001315204,0.0003133068,0.005633593,0.0004616139,0.6448216,0.07873678,0.2692047],"study_design_scores_gemma":[0.000004433836,0.00001302286,0.0001298992,0.0000703296,0.000006262642,0.0001580645,0.00005595823,0.01203719,0.0002612042,0.9023364,0.08491402,0.00001332317],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007686586,0.01281728,0.9224495,0.008024749,0.001038727,0.00004914861,0.0001366864,0.0008192467,0.05389608],"genre_scores_gemma":[0.06021962,0.02222594,0.7520203,0.005619711,0.008903953,0.0004661548,0.0006105633,0.001426087,0.1485077],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0162811,"threshold_uncertainty_score":0.05446571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385489604","doi":"10.1007/978-3-031-33390-3_1","title":"Prologue","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Economic Theory and Institutions","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Prologue; Computer science; Mathematics; Art; Literature","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03725975681368367,"gpt":0.227419429981194,"spread":0.1901596731675103,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005667417,0.00118616,0.0006373634,0.001448068,0.0009232871,0.003352671,0.0012989,0.0008531295,0.6024724],"category_scores_gemma":[0.003709809,0.0006076909,0.00067693,0.001136397,0.0004112867,0.003262048,0.002310431,0.001474051,0.4454255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000835478,"about_ca_system_score_gemma":0.001219475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002621555,"about_ca_topic_score_gemma":0.003310725,"domain_scores_codex":[0.9994339,0.0001159609,0.00003220271,0.0001326008,0.0002013082,0.00008397804],"domain_scores_gemma":[0.9993304,0.000217803,0.00002213484,0.000138896,0.0002086401,0.00008215438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001713458,0.00007103578,0.000198044,0.0003706405,0.000009245971,0.00006040134,0.00005732149,0.0003252356,0.0007448737,0.02485872,0.8394972,0.1336359],"study_design_scores_gemma":[0.00004786322,0.00001370946,0.0001003991,0.00007109853,0.000005853866,0.00008157305,0.00003401161,0.0006134969,0.0006738277,0.01281499,0.985532,0.00001117497],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002203997,0.0009279216,0.06552557,0.002373783,0.001477945,0.0005637479,0.05610467,0.05014315,0.8206792],"genre_scores_gemma":[0.02555893,0.001712627,0.07508866,0.002653736,0.0007915377,0.0008668,0.1105221,0.04672677,0.7360789],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6024724,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486397","doi":"10.1007/978-3-031-33390-3_7","title":"Nearest Neighbors","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"k-nearest neighbors algorithm; Computer science; Pattern recognition (psychology); Naive Bayes classifier; Artificial intelligence; Classifier (UML); Similarity (geometry); Data mining; Machine learning; Support vector machine","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02556425250823703,"gpt":0.2586592445469714,"spread":0.2330949920387343,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009693861,0.001072731,0.001439494,0.00303292,0.001577361,0.003506625,0.002219193,0.00189156,0.05380809],"category_scores_gemma":[0.005044371,0.0005848124,0.001159705,0.002969342,0.0006596121,0.003452942,0.001742615,0.00124809,0.04153769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007702143,"about_ca_system_score_gemma":0.001249758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004019079,"about_ca_topic_score_gemma":0.008667668,"domain_scores_codex":[0.9978856,0.0002137075,0.00009778121,0.0006222745,0.001029037,0.0001515701],"domain_scores_gemma":[0.9986635,0.0002367074,0.00006228151,0.0004823856,0.0004966872,0.00005837286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001834165,0.0001348607,0.0009779328,0.0002837245,0.00006939263,0.0001006172,0.0001059189,0.01455729,0.002867283,0.06615715,0.1153628,0.7991996],"study_design_scores_gemma":[0.00007298932,0.0001820731,0.001561863,0.0003207218,0.0001155657,0.001454704,0.0005484415,0.2279234,0.01415453,0.1876041,0.5659636,0.00009800724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01187606,0.005913408,0.6922019,0.0009844238,0.002037849,0.0007048462,0.005654114,0.005886271,0.2747411],"genre_scores_gemma":[0.1234055,0.003642172,0.564432,0.0008485681,0.0006355845,0.0004613431,0.01591032,0.001487552,0.289177],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05380809,"threshold_uncertainty_score":0.180006,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W58877873","doi":"10.1007/978-1-4614-9020-3_4","title":"Importing, Exporting and Producing Data","year":2013,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science","authors":[{"name":"Pierre Lafaye de Micheaux","is_ca":true},{"name":"Rémy Drouilhet","is_ca":false},{"name":"Benoît Liquet","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.124470994372565,"gpt":0.3666736128907216,"spread":0.2422026185181566,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002974075,0.001391381,0.00115617,0.003740184,0.0009293593,0.005543447,0.002054144,0.000915349,0.06374364],"category_scores_gemma":[0.01410243,0.001064942,0.001158871,0.007077789,0.0008937691,0.005170308,0.003709764,0.002531696,0.09317264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007340834,"about_ca_system_score_gemma":0.002746892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002028138,"about_ca_topic_score_gemma":0.002316117,"domain_scores_codex":[0.9981724,0.0002988843,0.0002344655,0.0003174243,0.0008880773,0.00008884466],"domain_scores_gemma":[0.9917869,0.003169649,0.0002146951,0.003279104,0.00137551,0.0001741955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008336236,0.00006530332,0.001416763,0.001300103,0.00003307455,0.0003497821,0.001094381,0.001069985,0.009397834,0.03683526,0.1665192,0.7818351],"study_design_scores_gemma":[0.0000165197,0.00002466833,0.001519098,0.0003427516,0.00003586586,0.0006532225,0.000376023,0.001807928,0.01678485,0.03562341,0.9427614,0.00005428852],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005361721,0.001606173,0.7240561,0.001827806,0.0008978879,0.0007820584,0.03190093,0.04438005,0.1891872],"genre_scores_gemma":[0.01722326,0.004713329,0.7817066,0.00115715,0.000482776,0.0007717786,0.04105434,0.03543881,0.117452],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06374364,"threshold_uncertainty_score":0.2132438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4252485560","doi":"10.1007/978-1-4614-9020-3_1","title":"Introducing R","year":2013,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"clone (Java method); Programming language; Computer science; Software; Window (computing); Code (set theory); Statistical software; Statistical analysis; Software engineering; Computer graphics (images); Mathematics; Operating system; Statistics; Biology","authors":[{"name":"Pierre Lafaye de Micheaux","is_ca":true},{"name":"Rémy Drouilhet","is_ca":false},{"name":"Benoît Liquet","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01125833925195088,"gpt":0.2346782035492136,"spread":0.2234198642972627,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003007255,0.002301348,0.001625227,0.003051755,0.0007337313,0.004425823,0.002294606,0.001609992,0.2077257],"category_scores_gemma":[0.01768557,0.000967285,0.001170392,0.00258613,0.001418634,0.00327025,0.001850414,0.003202972,0.2822849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006177712,"about_ca_system_score_gemma":0.002238435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009789757,"about_ca_topic_score_gemma":0.001204535,"domain_scores_codex":[0.99578,0.001388493,0.000315753,0.0008629438,0.001469429,0.0001833623],"domain_scores_gemma":[0.9931453,0.003037345,0.0003104258,0.001949936,0.001352176,0.0002048413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000230698,0.00001784051,0.0001349933,0.0006866412,0.00003101788,0.00007635685,0.0001221227,0.0009331589,0.0005403471,0.1454232,0.6159531,0.236058],"study_design_scores_gemma":[0.00001156494,0.00001024794,0.00008951896,0.0001687821,0.00001317532,0.0001327684,0.0000294367,0.0009348136,0.0004180508,0.09816931,0.9000031,0.00001931835],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000431752,0.007212831,0.6833936,0.003253264,0.003515799,0.0002558978,0.01295965,0.05545326,0.2335239],"genre_scores_gemma":[0.0133123,0.009079257,0.5729789,0.004473417,0.00309754,0.001523995,0.01963549,0.05112587,0.3247733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2077257,"threshold_uncertainty_score":0.6949118,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486431","doi":"10.1007/978-3-031-33390-3_2","title":"Statistical Learning: Concepts","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Statistical learning; Bayes' theorem; Interpretation (philosophy); Variance (accounting); Naive Bayes classifier; Supervised learning; Unsupervised learning; Statistics; Mathematics; Bayesian probability; Artificial neural network; Support vector machine","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.102508996654528,"gpt":0.4155633201201151,"spread":0.313054323465587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003565983,0.001732012,0.002048312,0.003201386,0.0009544721,0.005796789,0.001911583,0.001761487,0.01193685],"category_scores_gemma":[0.007782539,0.0008347247,0.001089784,0.003849131,0.006702619,0.006828843,0.003488214,0.005411584,0.008718705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674288,"about_ca_system_score_gemma":0.00205379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008123,"about_ca_topic_score_gemma":0.0005570684,"domain_scores_codex":[0.9967969,0.001168682,0.000275994,0.0005048366,0.001167395,0.00008622542],"domain_scores_gemma":[0.9956604,0.002749349,0.0001812769,0.0006921716,0.0005817804,0.0001350031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000004934073,0.00002304475,0.00009990603,0.0003047835,0.00001993722,0.00003234741,0.0001581722,0.001294282,0.0002041481,0.8850822,0.02905925,0.08371688],"study_design_scores_gemma":[0.00000324567,0.00001048167,0.00009493228,0.00008463857,0.000007231195,0.0000999292,0.00002904721,0.004211143,0.0001332591,0.9141129,0.08120476,0.000008447393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00108054,0.0328442,0.8893327,0.006366899,0.002047793,0.000123501,0.000519306,0.0009385021,0.06674668],"genre_scores_gemma":[0.1035596,0.06381086,0.6789005,0.01051559,0.021117,0.00201075,0.002079977,0.001540384,0.1164653],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01193685,"threshold_uncertainty_score":0.03993273,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486326","doi":"10.1007/978-3-031-33390-3_13","title":"Feature Engineering","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Feature engineering; Feature (linguistics); Computer science; Artificial intelligence; Logarithm; Data mining; Machine learning; Mathematics; Deep learning","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01220526921624156,"gpt":0.2435632484900194,"spread":0.2313579792737778,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004878564,0.0008728219,0.0005173896,0.00171213,0.0005130153,0.001946758,0.001162142,0.000563671,0.03846046],"category_scores_gemma":[0.003175587,0.000348173,0.001204182,0.001419548,0.0002714927,0.002116424,0.001612593,0.001234479,0.0219074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005879911,"about_ca_system_score_gemma":0.0008642832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002124502,"about_ca_topic_score_gemma":0.002992832,"domain_scores_codex":[0.999337,0.00005189933,0.0000402925,0.0002139409,0.000279332,0.00007743907],"domain_scores_gemma":[0.9992992,0.0001410994,0.00002606337,0.0002632197,0.0002399387,0.00003044503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001412061,0.0001174076,0.001286268,0.0002158697,0.00005140648,0.0001772334,0.00009234257,0.004338247,0.0124003,0.03112411,0.09539714,0.8546584],"study_design_scores_gemma":[0.00006730815,0.0001782886,0.002803991,0.0001477763,0.0001245715,0.000941119,0.000214573,0.1563562,0.07496801,0.1330849,0.6310409,0.00007248925],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01113337,0.0003789352,0.8955999,0.0005393068,0.0003894677,0.0004962615,0.008065662,0.03637066,0.04702638],"genre_scores_gemma":[0.1768625,0.0008475803,0.6194766,0.0008812847,0.0002113702,0.0008848712,0.04176397,0.008518859,0.1505531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03846046,"threshold_uncertainty_score":0.1286631,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385486392","doi":"10.1007/978-3-031-33390-3_11","title":"Boosting","year":2023,"lang":"en","type":"book-chapter","venue":"Statisctics and computing/Statistics and computing","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Boosting (machine learning); Gradient boosting; Multinomial logistic regression; Artificial intelligence; Computer science; Machine learning; AdaBoost; Random forest; Mathematics; Classifier (UML)","authors":[{"name":"Matthias Schonlau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03486239759677846,"gpt":0.2658821743200783,"spread":0.2310197767232999,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001086591,0.000838288,0.001247651,0.001110525,0.0008043572,0.001807769,0.00154063,0.001278757,0.05579492],"category_scores_gemma":[0.003963874,0.0004694403,0.0008577213,0.001236528,0.0005912289,0.001267054,0.001293586,0.001964336,0.03423324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005389618,"about_ca_system_score_gemma":0.000819378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001109996,"about_ca_topic_score_gemma":0.00179208,"domain_scores_codex":[0.9991292,0.0002127761,0.00002158562,0.0002519278,0.0002876039,0.00009689377],"domain_scores_gemma":[0.9990613,0.0002652357,0.0000342074,0.0003393987,0.0002270807,0.00007273213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001178642,0.000104113,0.0003537794,0.000183249,0.00007872094,0.000035883,0.00004023829,0.02487696,0.001981569,0.1485119,0.1903915,0.6333242],"study_design_scores_gemma":[0.00007667602,0.00009659206,0.0006537407,0.000123807,0.00008254081,0.0002951379,0.00003036374,0.2385848,0.00454663,0.3366602,0.4188155,0.0000341019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004771165,0.004591346,0.7952481,0.001473193,0.001932502,0.0002357822,0.001427895,0.007425141,0.1828948],"genre_scores_gemma":[0.1423851,0.003606331,0.5642023,0.003004943,0.002217228,0.0005091664,0.00480698,0.003572033,0.2756958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05579492,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}